Solution of nonlinear mixed integral equation via collocation method basing on orthogonal polynomials
1Department of Mathematics, Faculty of Applied Science, Umm Al- Qura University, Makkah, Kingdom of Saudi Arabia.
Heliyon
|December 6, 2022
Summary
This study proves a unique solution for nonlinear mixed integral equations (NMIEs) and analyzes its stability. Numerical methods using Hermite and Laguerre polynomials provide accurate results and error estimates for various NMIE applications.
Area of Science:
- Applied Mathematics
- Numerical Analysis
- Integral Equations
Background:
- Nonlinear mixed integral equations (NMIEs) present complex challenges in mathematical physics and engineering.
- Understanding the existence and stability of NMIE solutions is crucial for accurate modeling.
Purpose of the Study:
- To establish the existence and uniqueness of a solution for a third-kind NMIE in position and time.
- To investigate the stability of the obtained solution.
- To develop and analyze numerical methods for solving NMIEs.
Main Methods:
- Transformation of the NMIE into a system of nonlinear integral equations (NIEs) using a quadratic method.
- Application of a collocation method with Hermite and Laguerre polynomials to derive nonlinear algebraic systems (NAS).
- Error estimation for the developed numerical techniques.
Main Results:
- Demonstrated the existence and uniqueness of a solution for the studied NMIE.
- Developed two distinct NAS based on Hermite and Laguerre polynomials.
- Computed numerical results and calculated error estimates for various NMIE types.
Conclusions:
- The proposed numerical methods, utilizing Hermite and Laguerre polynomials, effectively solve NMIEs.
- The study provides a robust framework for analyzing the existence, uniqueness, and stability of NMIE solutions.
Keywords:
Hermite polynomialsLaguerre polynomialsNonlinear algebraic systemNonlinear mixed integral equationMore Related Videos
Related Concept Videos
Linear Approximation in Time Domain
114
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
114
Linear Approximation in Frequency Domain
123
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
123
Routh-Hurwitz Criterion II
341
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
341
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
96
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
96
Pole and System Stability
377
The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...
377
Poisson's And Laplace's Equation
3.3K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
3.3K


